www.atmos-chem-phys.net/16/7171/2016/ doi:10.5194/acp-16-7171-2016
© Author(s) 2016. CC Attribution 3.0 License.
Characterization of total ecosystem-scale biogenic VOC exchange at
a Mediterranean oak–hornbeam forest
Simon Schallhart1, Pekka Rantala1, Eiko Nemitz2, Ditte Taipale1,3,4, Ralf Tillmann5, Thomas F. Mentel5, Benjamin Loubet6, Giacomo Gerosa7, Angelo Finco7, Janne Rinne1,8,9,10, and Taina M. Ruuskanen1
1Department of Physics, University of Helsinki, Helsinki, Finland 2Centre for Ecology & Hydrology (CEH), Penicuik, UK
3Department of Plant Physiology, Estonian University of Life Sciences, Tartu, Estonia 4Department of Forest Sciences, University of Helsinki, Helsinki, Finland
5Forschungszentrum Jülich GmbH, Jülich, Germany
6National Institute of Agronomic Research UMR ECOSYS INRA, AgroParisTech, Université Paris Saclay,
Saint-Aubin, France
7Department of Mathematics and Physics, Catholic University of Brescia, Brescia, Italy 8Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland 9Finnish Meteorological Institute, Helsinki, Finland
10Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
Correspondence to:Simon Schallhart (simon.schallhart@helsinki.fi)
Received: 21 August 2015 – Published in Atmos. Chem. Phys. Discuss.: 14 October 2015 Revised: 5 April 2016 – Accepted: 16 May 2016 – Published: 10 June 2016
Abstract. Recently, the number and amount of biogenically emitted volatile organic compounds (VOCs) has been dis-cussed in great detail. Depending on the ecosystem, the pub-lished number varies between a dozen and several hundred compounds. We present ecosystem exchange fluxes from a mixed oak–hornbeam forest in the Po Valley, Italy. The fluxes were measured by a proton transfer reaction-time-of-flight (PTR-ToF) mass spectrometer and calculated using the eddy covariance (EC) method. Detectable fluxes were observed for up to 29 compounds, dominated by isoprene, which comprised over 60 % of the total upward flux (on a molar basis). The daily average of the total VOC upward flux was 10.4 nmol m−2s−1. Methanol had the highest concentra-tion and accounted for the largest downward flux. Methanol seemed to be deposited to dew, as the downward flux hap-pened in the early morning, right after the calculated surface temperature came closest to the calculated dew point temper-ature.
We estimated that up to 30 % of the upward flux of methyl vinyl ketone (MVK) and methacrolein (MACR) originated from atmospheric oxidation of isoprene. A comparison be-tween two methods for the flux detection (manual and
au-tomated) was made. Their respective advantages and disad-vantages were discussed and the differences in their results shown. Both provide comparable results.
1 Introduction
Volatile organic compound (VOC) fluxes between vegeta-tion and atmosphere affect atmospheric chemistry by con-trolling the oxidation capacity of the atmosphere (Fehsenfeld et al., 1992; Fuentes et al., 2000). The non-methane biogenic VOC emissions are dominated by terpenoids, e.g., isoprene and monoterpenes, followed by oxygenated VOCs such as methanol and acetone (Kesselmeier et al., 1999; Guenther et al., 2012). The emitted VOCs are physically removed by dry or wet deposition or are oxidized by, for example, OH, O3 and NO3(Mogensen et al., 2015). Their oxidation
con-tributes to the tropospheric ozone formation and destruction processes (e.g., Derwent et al., 2003; Bloss et al., 2005), aerosol formation and aerosol growth and thereby influences air quality and climate (Kulmala et al., 1998; Tunved et al., 2006; Monks et al., 2009; Riipinen et al., 2012; Paasonen
et al., 2013). To assess these effects caused by the biogenic VOCs, reliable flux budgets are necessary.
Most ecosystem-scale VOC flux measurements have been conducted with disjunct eddy covariance method by mass scanning using proton-transfer-reaction quadrupole mass spectrometer (PTR-QMS), relaxed eddy accumulation or sur-face layer gradient techniques with gas chromatography– mass spectrometry applying selected ion mode (e.g., Lamb et al., 1985; Businger and Oncley, 1990; Fuentes et al., 1996; Guenther et al., 1996; Rinne et al., 2001; Karl et al., 2002; Rinne and Ammann, 2012). These methods require pre-selection of target compounds and in the case of the PTR-QMS suffer from the limitation of unit mass resolution, mak-ing it impossible to separate isobaric compounds, i.e., com-pounds with identical integer mass but different chemical composition. Thus, measurements have inherently focused on compounds already known to be emitted by vegetation and thereby hinder the discovery of fluxes of compounds not previously known to be emitted by vegetation. Furthermore, extreme weather conditions such as hail can change the VOC flux pattern (Kaser et al., 2013), which is difficult to measure with such methods.
Lately, new insights have been provided by the more uni-versal and sensitive PTR-ToF. Park et al. (2013) analyzed flux data obtained by the PTR-ToF and revealed many pre-viously unobserved compounds to be emitted, but this ap-proach has so far only been applied to very few vegetation types (e.g., Ruuskanen et al., 2011; Park et al., 2013; Kaser et al., 2013).
In this study we have conducted VOC flux measurements at a remnant natural oak–hornbeam-dominated forest (Bosco Fontana) in northern Italy as part of an intensive field cam-paign organized by the European FP7 project “ÉCLAIRE” (Effects of Climate Change on Air Pollution Impacts and Response Strategies for European Ecosystems). The objec-tives of the ÉCLAIRE Bosco Fontana experiment were (a) to quantify the exchange of a range of pollutants with this ecosystem in one of the most polluted regions of Europe, (b) to assess the importance of in-canopy chemical inter-actions on the biosphere–atmosphere exchange of reactive gases and aerosols and (c) to provide a supersite in the frame-work of a spatial Po Valley study that combined resources from two EU projects (ÉCLAIRE and PEGASOS – Pan-European Gas-AeroSOls-climate interaction Study) with a national Italian initiative.
In this paper, we present the results of the application of state-of-the-art PTR-ToF mass spectrometry and eddy co-variance technique to derive the total biogenic VOC flux above the Bosco Fontana ecosystem. The aims of this study were (i) the comparison of two data-processing approaches to identify compounds for which fluxes were above the detec-tion limit, contrasting the automated method used by Park et al. (2013) with the manual method, which uses manual cross covariance peak checking (e.g., Taipale et al., 2010; Ruuska-nen et al., 2011; Kaser et al., 2013); (ii) the characterization
of the total ecosystem-scale VOC flux from a Mediterranean oak forest, with particular emphasis on (iii) the quantification of the contribution of non-terpenoid VOCs to the total VOC flux; (iv) the estimation of the possible contribution of sec-ondary compounds to the observed above-canopy fluxes; and (v) the study of dew potentially causing methanol deposition in the morning.
A companion paper (Acton et al., 2016) compares the PTR-ToF-MS measurements with simultaneous measure-ments by PTR-QMS and a bottom-up estimate of the canopy flux scaled up from leaf-level emission measurements, and also derives emission factors for the use in emissions mod-els.
2 Materials and methods
2.1 Bosco Fontana site description
The measurements were performed from 15 June to 6 July 2012 in Bosco Fontana, Lombardy, Italy. Bosco Fontana is a 233 ha forested nature reserve located in the northeast of the Po Valley. The main tree species are Quercus cerris(turkey oak), Quercus robur (pedunculate oak), Quer-cus rubra (northern red oak) and Carpinus betulus (horn-beam) (Dalponte et al., 2008). The typical height of the trees varied between 26 and 28 m. The surroundings of the Bosco Fontana forest area are agricultural land and some roads. The largest city nearby is Mantua, with 48 000 inhabitants, which is located 8 km to the southeast. The measurement site is 25 m above sea the level. The temperatures varied from 18 to 32◦C during the campaign and the main wind directions were east and west. The measurement tower was 42 m high and located in the southwestern part of the nature reserve (45.20◦N, 10.74◦E). Figure 1 shows a satellite image of the area, with the position of the tower and the mean 80 % foot-print (Acton et al., 2016). The climatological mean annual temperature is 13.3◦C and the mean annual precipitation is 834 mm (Willmott and Matsuura, 2012a, b).
2.2 Meteorological and trace gas data
The measurement tower was equipped with temperature and relative humidity sensors at several heights. The turbulence data were measured with a 3-D anemometer (HS 50, Gill In-struments) at 32 m above ground level (hereafter a.g.l.) and the instrument was fastened on a pole 1.7 m away from the lattice tower. An additional measurement of wind direction was provided by a 2-D ultrasonic anemometer as part of an integrated weather station (Weather Transmitter WXT610, Vaisala; 32 m a.g.l.), which also measured air pressure, rela-tive humidity and temperature. The O3concentration was
de-termined with a chemiluminescence analyzer (model 202, 2B Technologies) at 40 m a.g.l. and NO2and NO with a
chemi-luminescence analyzer equipped with a thermal converter (model 42C, Thermo Scientific) at a height of 32 m a.g.l.
Figure 1. Satellite picture (imagery© 2015 Cnes/Spot Image, DigitalGlobe, European Space Imaging, Landsat, map data©2015 Google) of the Bosco Fontana national park and the surroundings. The position of the flux tower is marked by a white cross and sur-rounded by the mean 80 % of the flux footprint, which is represented by the white line (Acton et al., 2016). The dark green surrounding is the forest area of the national park.
Carbon dioxide flux measurements were performed, us-ing the eddy covariance technique, at the top of the tower, where a sonic anemometer (USA 1, Metek) and a fast IRGA analyzer (model 7500, LICOR) were mounted on a pole, at a distance of 1.7 m from the edge of the tower. The sonic anemometer was working at 20 Hz and the fast IRGA was calibrated before and after the field campaign and no signif-icant drift was observed. Carbon dioxide fluxes were mea-sured from 18 June to 12 July. Several procedures were ap-plied in order to obtain the correct flux calculations: despik-ing (Vickers and Mahrt, 1997), double rotation of the ref-erence system (Kaimal and Finnigan, 1994), linear detrend-ing (Lee at al., 2004), frequency loss corrections usdetrend-ing the ogive methodology (Ammann et al., 2006), Webb–Pearman– Leuning (WPL) corrections for density fluctuations (Webb et al., 1980), and stationarity test (Foken and Wichura, 1996); finally, a manual selection of the data was performed too and data after rainfalls were discarded.
The dew point temperature, Td, was calculated according
to Lawrence (2005): Td=T 1 −Tln RH 100 Lvap Rw −1 , (1)
where T is the ambient temperature, RH is the rel-ative humidity, Lvap is the enthalpy of vaporization
(2.501 × 106J kg−1)and Rwis the gas constant of water
va-por (461.5 J K−1kg−1).
The average aerodynamic temperature T z00 was esti-mated using a method described by Nemitz et al. (2009) as T z00 = T + θ0w0(R
a+Rb) , (2)
where z00is the notional mean height of the canopy exchange, θ0w0 and T are the measured heat flux and temperature at
the measurement height of zm, respectively. In this study,
the roughness length z0 was estimated to be ca. 1 m (e.g.,
Dolman, 1986). For the zero displacement height d we used the common approximation of d = 2/3 × zc, where zcis the
canopy height (28 m).
The resistance parameters Raand Rbwere determined as
(Owen and Thompson, 1963; Garland, 1977)
Ra= u u2 ∗ − 9h zm−d L −9m zm−d L ku∗ (3) and Rb= (Bu∗)−1, (4)
where L is the Obukhov length. The sublayer-Stanton num-ber (B) can be estimated by
B−1=1.45Re0.24∗ Sc0.8, (5)
where roughness Reynolds number, Re∗, is given by
Re∗=
z0u∗
υ (6)
and the Schmidt number, Sc, by Sc = υ
D. (7)
The friction velocity u∗ and the horizontal wind u were
taken from the measurements at zm and k is von Kármán’s
constant (0.4). The kinematic viscosity of air, υ, was as-sumed to be constant (≈ 1.56×10−5m2s−1), as was the ther-mal diffusivity temperature in air, D (≈ 1.9 × 10−5m2s−1). The integral stability correction functions 9h and 9m for
heat (h) and momentum (m), respectively, were taken from Rannik (1998).
2.3 VOC measurements 2.3.1 PTR-ToF measurements
The PTR-ToF (Ionicon Analytik GmbH; Graus et al., 2010; Jordan et al., 2009) combines the soft ionization of a PTR source with the high mass resolution of a time-of-flight mass spectrometer ∼ 4500 m/1m (determined as the full width at half maximum of the ion peak). The precise mass of a com-pound can be derived from the time of flight and the elemen-tal composition can be calculated from the observed mass defect. Therefore the instrument can separate isobaric com-pounds. It cannot, however, distinguish between isomeric
compounds, as it gives no information about the compound structure. The retime measure of full spectra at 10 Hz al-lows for flux measurements with the eddy covariance tech-nique.
The PTR-ToF was placed inside a container next to the measurement tower. Air from 32 m height was sampled through a 40 m long and 9.5 mm wide (inner diameter; i.d.) PTFE tube (hereafter referred to as common sampling line), which was pumped at 63 L min−1. The pressure drop in-duced by the pumping was sufficient to prevent condensa-tion in the sampling line outside of the container. Inside the air-conditioned container, the inlet line was heated (self-regulated heating wire with 11 W m−1 at 30◦C). The 3-D wind measurements were obtained with a frequency of 10 Hz 10 cm above the inlet.
The PTR-ToF was connected to the inlet line via a three-way valve (type 6606 with ETFE, Bürkert GmbH & Co. KG), from where a subsample of 0.5 L min−1 was pumped through a 1.6 mm (i.d.) and 1 mm (i.d.) PEEK capillary (to-gether around 20 cm long; heated between 40 and 60◦C) to the instrument. The PTR-ToF used a 30 mL min−1flow for analysis, the remaining flow was discarded and served only as a by-pass flow in order to decrease the response time of the PTR-ToF and associated wall losses in the inlet capillar-ies. The drift tube was operated at 600 V and temperature of 60◦C. Together with a drift tube pressure of 2.3 mbar this resulted in an EPTR/N ratio of 130 Td, where EPTR is the
electrical field strength and N is the gas number density. The instrument produced a time series of 22 days, with a 1.5-day break when the air-conditioning in the container failed. 2.3.2 Calibration and concentration calculation The instrument background was measured one to three times per day. A small pump (N86KNE, KNF Neuberger) estab-lished a 1.4 L min−1flow from the common sampling line to a custom-made catalytic converter. This converter was heated to 350◦C and created VOC-free (zero-) air at ambient hu-midity. The zero air was connected to the second port of the three way valve and passed an overflow in order to achieve a constant zero-air flow at a constant pressure (Fig. 2). The background measurements were used for the calculations of the concentrations as well as the determination of the limit of detection.
The instrument was calibrated every second week, i.e., a total of three times. A custom-built calibration unit, which mixed zero air with the calibration gas, was inserted between the catalytic converter and the overflow (Fig. 2). The calibra-tion gas (Apel Riemer Environmental Inc.) contained 16 dif-ferent compounds with the mass range from 33 to 180 amu at known concentrations of around 1 ppm. As the gas was diluted with zero air (calibration gas: 10 mL min−1; zero air: 1.4 L min−1) the resulting mixing ratios were around 7 ppb. The sensitivities were calculated from the observed count rates of the zero air and the calibration gas
measure-Figure 2. Schematic diagram of the inlet of the PTR-ToF used for the VOC measurements. Red lines indicate heated tubing.
ments in the ppb range. For the VOCs that were not included in our calibration standard, we used average sensitivities for compound families CxHy (based on isoprene, benzene,
toluene, o-xylene, trimethylbenzene, naphthalene, α-pinene combined with C6H+9 fragment), CxHyOz (considering
ac-etaldehyde, acrolein, acetone, 2-butanone) and CxHyNz (set
to that of acetonitrile). The averaged sensitivities were CxHy
=13 (±1.7) ncps ppb−1, CxHyOz=19.1 (±1.3) ncps ppb−1
and CxHyNz=18.1 (±1.3) ncps ppb−1. The ranges given in
the brackets are the standard deviations of the average sen-sitivities calculated for the compounds in each group from the calibrations. Normalized counts per second (ncps) have been corrected for transmission (pusher duty cycle losses) and primary ion fluctuations (Herbig et al., 2009). The aver-age signal of the primary ion signal (upscaled via the isotope H183 O+1) was around 750 000 cps (transmission corrected: 107cps). The impurities were O+2 4 % and NO+0.3 % of the H3O+primary ion. The monoterpene sensitivities were
de-rived from the α-pinene calibrations (in the calibration gas). Fragments from compounds in the calibration standard were not taken into account when calculating the sensitivities. For example, the signal at C5H+9 (m/z 69.0699) relates to the
protonated parent ion of isoprene and is scaled up to the total isoprene, although some isoprene fragments also show up at other masses. As a consequence, it is important that fluxes at those fragments are excluded to avoid double counting. The result of this procedure can be found in Table A1 in the Ap-pendix.
For compounds/fragments not included in the calibration standard, the average sensitivities for the compound families are applied as previously described. In this case the fragmen-tation pattern is not accounted for and all fragments have to be added up to arrive at the total flux, excluding those that could be associated with calibrated compounds. Two excep-tions were made: the sensitivity for acetic acid was halved (9.55 ncps ppb−1)as it was not calibrated and its major frag-ment (C2H3O+; Baasandorj et al., 2015) was disregarded.
For the data post-processing the ToF Analyzer V2.45 software was used, which has been described in Müller et al. (2010, 2013). A peak list (Table A1) was created with the TofTool software (Junninen et al., 2010) by integrating the 10 Hz raw data for 1 h and then fitting and identifying the different peaks. The measured mass peaks were identified by matching them with the calculated masses of different com-binations of H, C, O, N and S atoms. The range of atoms allowed to appear in a compound was set from 0 to 50.
After peak fitting was performed on the 10 Hz data the out-put of the ToF analyzer were aggregated to provide 30 min concentration data in a three-step process. First, the 10 Hz data were averaged over 30 min. Second, from these 30 min data the zero-air measurements were subtracted, wherein val-ues for the times between the zero-air measurements were linearly interpolated. The resulting signals were then com-pared to the limit of detection LOD = 2σzero, where σzero
is the standard deviation of the 10 Hz zero-air signal during a 30 min measurement. Third, to calculate the volume mix-ing ratios, all compounds above the LOD were divided by the measured or assigned sensitivity. Compounds with sig-nals below the LOD were disregarded from the further anal-ysis (Table A1). For the concentration measurements 71 (out of 163) masses were above the LOD. In the case of the flux measurements, 57 out of 163 showed flux behavior and 36 (out of 57) masses were above the LOD. The rejected masses also include primary ions and impurities from the ion source (Table A1).
2.3.3 Flux calculations
Fluxes were derived using the eddy covariance (EC) method. In EC, the flux is calculated using a discretized covariance:
w0c0=1 n n X i=1 w0(i) c0 i + λ 1t , (8)
where w0 and c0 are high-frequency fluctuations of vertical wind and concentration, respectively, i the number of the measurement, n is the sum of all measurements during the flux averaging time (30 min in this study), 1t is the sampling interval (0.1 s) and λ is the lag time caused by the sampling tubes (e.g., Kaimal and Finnigan, 1994).
In this study, vertical wind and VOC concentrations were both recorded at 10 Hz frequency. The flux calculation pro-cedure was as follows.
First, the wind vector was 2-D rotated using the method described by Kaimal and Finnigan (1994). If the vertical ro-tation was more than 5◦, the period was flagged and after the
“compound with exchange” detection rejected from further analysis. Data which were measured during periods when the wind was coming through the tower was not filtered out (dis-cussed in Acton et al., 2016).
The linear trend was removed from the concentrations while block averaging was used for the vertical wind mea-surements.
Next, cross covariance functions (CCFs) were calculated between the vertical wind and each of the volume mixing ratios for every 30 min measurement period. The lag time and compounds for which the flux was deemed detectable were identified with two different methods (Table 1), which are termed the “manual method” (Taipale et al., 2010) and the “automated method” (Park et al., 2013).
For the manual method the lag time was determined for each 30 min periods and compounds individually by maxi-mizing the smoothed CCF from a lag time window of 0–5 s (Taipale et al., 2010). The smoothing of the CCF decreases possible flux overestimation caused by noise when using the maximum covariance method (Taipale et al., 2010; Langford et al., 2015). In the next step, compounds with detectable flux were identified by checking the CCFs manually for each individual compound and for several different 30 min peri-ods. The total number of manually identified CCFs was well over 1000. Compounds for which a clear CCF maximum was found were used for the further flux calculations.
For the automated method, such as in Park et al. (2013), a constant lag time (2.6 s) was used for all compounds and all 30 min measurement points. This avoids overestimation in the flux, which can happen if the maximizing method is used for flux values close to the detection limit (Langford et al., 2015). This lag time used here was calculated from the averaged absolute CCF of isoprene, which exhibits a clear maximum. The individual 30 min lag times from the se-lected time window were also calculated to confirm that the lag time did not shift during the campaign. To identify the compounds with detectable flux, an automated flux search-ing routine was used. First, absolute CCFs for each com-pound were calculated using daytime values from 10:00 to 16:00 (CET), i.e., when good conditions for turbulence and high flux are present (see Fig. A5; Park et al., 2013). Next, the absolute values of the 30 min CCFs in this time window were averaged over the entire measurement period (Figs. A6 to A9). From this averaged CCF, the routine automatically calculated the flux (at 2.6 s lag time), the average noise and the standard deviation of the noise (σnoise). The mean and
standard deviation of the noise were determined from areas at the left and right border of the CCF spectra (Fig. A6 to A9). Finally, the mean noise was subtracted from the flux and then divided by σnoise. For ratios > 3 the respective
com-pound was used for the further flux calculations.
The final flux values were calculated for each method from the original (not smoothed or absolute) 30 min CCFs by us-ing the respective lag time and compound. The fluxes of both methods were then filtered using the stationarity criteria in-troduced by Foken and Wichura (1996): every 30 min period was divided into six 5 min subperiods and VOC fluxes were calculated for each 5 min period. If the flux values calculated using 5 min averages differed by more than 30 %, the period
Table 1. Comparison between the manual and the automated method for calculating VOC fluxes. QC means quality criteria and 203 is the number of 30 min periods measured between 10:00 and 16:00.
Step Manual Automated
Detection of compounds with significant flux:
detection of significant flux manual automated
number of checks per compound >20 1
amount of data used per check 30 min 203 × 30 min maximum of found masses in the literature∗ 10–20 ca. 500 Flux data calculation:
lag time variable (0–5 s) constant (2.6 s)
wind vector rotation 2-D 2-D
QC: vertical rotation 5◦ 5◦
QC: stationary criteria 70 % 70 %
∗The number of compounds with exchange is dependent on many site-specific factors (e.g., ecosystem, meteorological characteristics).
was disregarded from further analysis. The stationarity cri-teria together with the 5◦tilt angle disregarded 43 % of the
data for each method.
For those compounds for which a flux could be detected, the uncertainty of the flux was calculated from the two 60 s time windows at the border of the CCFs for each 30 min flux value. The root mean square of each window was calculated and the results averaged. This follows the approach of Lang-ford et al. (2015) and ensures that offsets (from zero) from the noise in the CCF tails are taken into account. For estima-tion of the uncertainty of the diurnal net flux, it was assumed that the errors of different flux values are independent, and the uncertainty can be calculated with the Gaussian propaga-tion of error. The independence assumppropaga-tion is not fully cor-rect, as fluxes from different compounds are derived using the same vertical wind data.
For the calculation of the diurnal 1 h flux data, all mea-surements which passed the quality checks were averaged. The daily average was then calculated by averaging the di-urnal data. This ensures that periods which have fewer data points (due to quality criteria filtering, background or calibra-tion measurements) do not get underrepresented in the daily average.
2.3.4 Spectral corrections
Due to the high-frequency attenuation and low-frequency cutoff, the measured EC flux underestimates the real flux (e.g., Moore, 1986; Horst, 1997). High-frequency attenua-tion is caused by the tubing, the sensor separaattenua-tion and the time response of the instrument itself, while low-frequency attenuation is caused by linear detrending or block averag-ing.
The effect of low-pass filtering can be quantified by the use of a transfer function. Formally the transfer function Hwccan
be written as Hwc(f ) = Cwc(f ) w0c0 × Cwθ(f ) w0θ0 −1 , (9)
where Cwcand Cwθare the cospectra of a scalar c and w, and
potential temperature θ and w, respectively. w0c0 and w0θ0
are “unattenuated” turbulent fluxes of a scalar and tempera-ture, respectively, and f is the frequency. A commonly used approximation for the first-order transfer function is (Horst, 1997)
Hwc≈
h
1 + (2π τf )2i−1, (10)
where τ is a system response time.
In this study, we determined the high-frequency attenua-tion using a method described by Horst (1997). In the method the attenuation factor α is calculated by the equation α =(w 0c0) a w0c0 = 1 1 + 2π nmτ u zm−d β, (11)
where zm is the measurement height (32 m), d the zero
displacement height (d = 2/3 × zc, where zc is the canopy
height, 28 m), u is the mean horizontal wind speed, (w0c0) a
is the attenuated flux and w0c0is the real flux. For neutral and
unstable stratification (zm−d)/L ≤0, β = 7/8, nm=0.085,
and for stable stratification, (zm−d)/L >0, β = 1, nm=
2.0 − 1.915/[1 + 0.5(zm−d)/L].
We selected daytime (10:00–16:00 CET wintertime), un-stable (w0T0>0) periods, and calculated cospectra of
tem-perature, isoprene and water clusters for every 30 min inter-val. Response times of isoprene and water clusters were then derived by using Eq. (10) and the median transfer functions (Eq. 9). After that, the flux losses were derived using the cor-rection factor α−1(Eq. 11) and the response time of the iso-prene measurements, τ = 1.1 s (Fig. 3). This value is similar
10-2 10-1 100 0 0.2 0.4 0.6 0.8 1 Frequency [Hz] T ra n s fe r f u n c ti o n M easured Hwc Fitted Hwc 95 % CI = 0.8 0.3 s 10-2 10-1 100 -0.2 0 0.2 0.4 0.6 0.8 1 Frequency [Hz] T ra n s fe r f u n c ti o n M easured Hwc Fitted Hwc 95 % CI = 0.9 0.3 s 10-2 10-1 100 0 0.2 0.4 0.6 0.8 1 Frequency [Hz] T ra n s fe r f u n c ti o n M easured Hwc Fitted Hwc 95 % CI = 1.1 0.3 s
Figure 3. Transfer functions of H3O+H2O (37.0284 amu; top), H3O+(H2O)2(55.039 amu; middle) and C5H8H+ (69.0699 amu; bottom). The circles are the measurements, the solid black line the fitted transfer function and the dashed lines are the 95 % confidence intervals. The response time of the measurement system, τ , was cal-culated by fitting Eq. (10) to the data.
to that obtained by Rantala et al. (2014), who utilized dis-junct EC with a PTR-QMS. The correction factor α−1 was finally multiplied to each VOC flux value. During daytime, the factor was mostly less than 10 %, and during nighttime typically around 20 %, resulting in overall correction of the isoprene flux, dominated by daytime emission, of 11 %.
The value τ = 1.1 s represents the response time of the whole system including the instrument and the inlet line. Therefore, the response time should be determined for each measurement setup individually as, for example, the length of the inlet line has an effect on the attenuation (Nordbo et al., 2013, 2014). The response time is also probably com-pound dependent because the attenuation of water vapor in-creases as a function of relative humidity (Mammarella et al., 2009). Thus, similar kind of behavior could be expected for water solvable compounds, such as methanol. In that sense,
the high-frequency corrections should be taken as rough es-timates.
2.3.5 Flux loss due to chemical degradation
The chemical degradation of different VOCs is dependent on their concentration, reaction rates and concentrations of ox-idants (O3, NO3, OH). Therefore the proxy for OH
concen-tration, [OH]proxy, was calculated according to Peräkylä et
al. (2014) and Petäjä et al. (2009):
[OH]proxy=5.62 × 105×UVB0.62. (12)
The calculated average midday concentration of the [OH]proxywas 1 × 106# cm−3.
As the UVB radiation was not measured directly dur-ing the Bosco Fontana study, an upper limit calculation was made by using the tropospheric ultraviolet model 4.1 (TUV; Madronich, 1993; Madronich and Flocke, 1999). The model was used via http://cprm.acom.ucar.edu/Models/ TUV/Interactive_TUV, using the pseudo-spherical discrete ordinate four-stream radiation transfer model and an albedo of 0.1. The NO3concentration was calculated as described
in Peräkylä et al. (2014) from the measured concentrations of NO2and O3.
The influence of chemical degradation on the measured EC fluxes depends on the relative magnitude of the chemical lifetime of the measured compound and its transport time. The transport time is the time the compound needs to get from its emission point to the actual measure point, and it can be characterized by turbulent mixing timescale. The ef-fect is often assessed using the Damköhler number (Damköh-ler, 1940), which is the ratio of the mixing timescale to the chemical lifetime. The smaller the Damköhler number is, the less influence the chemical degradation has on the flux. How-ever, since both the transport time and the chemical lifetime are height dependent, a more accurate assessment of the loss is achieved by calculating the ratio of the flux at the mea-surement height (F ) to the true surface exchange (E), e.g., using a stochastic Lagrangian transport model (Rinne et al., 2012). In Bosco Fontana, the F /E for isoprene was 0.95– 0.97, indicating that the measured fluxes are between 3 and 5 % lower than the emission due to the chemical degrada-tion. For the monoterpenes (we used α-pinene), which have the lowest F /E ratio (shortest lifetime) of the measured com-pounds, the F /E was between 0.8 and 0.95. No corrections for the chemical degradation have been made in this study. 2.4 Modeled MVK/MACR production
After having quantified the average fraction of the isoprene flux lost between point of emission and the measurement height, this section uses an alternative method to quantify the fraction of the observed MVK/MACR flux that is expected to be produced by the atmospheric oxidation of isoprene below
Figure 4. Diurnal flux plot of the manual method (top panel) and the automated method (bottom panel). For the automated method, the 10σnoisecompounds are plotted individually, while the remaining 18 compounds, which passed the 3σnoisethreshold (see Table A1), are summed up and plotted as “other”. All individual flux compounds are listed in Table 2.
the measurement height. This is done by integration of the chemical kinetic equation.
The chemical destruction of isoprene FQ, in an air column
below the measurement level can be calculated as FQ= zm Z 0 X i ki[Ri] [C5H8] dz, (13)
where ki is the rate constant, [Ri] is the concentration of
re-actant i, and [C5H8] is the concentration of isoprene. The
integration is done from surface to the measurement height zm. Even though [C5H8] and [Ri] are height dependent
(An-dronache et al., 1994; Hens et al., 2014), we assumed con-stant reactant and isoprene concentrations for the integration range as no profiles were measured in order to get an order of magnitude estimate. The chemical destruction was estimated for two ranges: from the ground level 0 to the measurement height and from the notional height (d + z0)to the
measure-ment height. The angle brackets, hi, indicate that the values are constant:
Q =kih[Ri]ih[C5H8]izm, (14)
Q =kih[Ri]ih[C5H8]i (zm−d −z0) . (15)
Due to the smaller reaction rates and the lower MVK/MACR concentration, we did not calculate the chemi-cal destruction of MVK/MACR.
In order to estimate the yield of MVK and MACR from isoprene, we selected the isoprene chemistry
mech-anism from the Master Chemical Mechmech-anism (MCM) v3.2 (Jenkin et al., 1997; Saunders et al., 2003), available via http://mcm.leeds.ac.uk/MCM/. The concentrations of MVK and MACR were calculated using the Kinetic PreProcessor (Damian et al., 2002) coupled to the box model MALTE-BOX (Boy et al., 2013). The simulation was executed using an initial concentration of isoprene (5.33 × 1010# cm−3; measured) and constant concentrations of OH (1 × 106# cm−3; calculated), O3 (2 × 1012# cm−3;
measured), NO (3.5 × 109# cm−3; measured), NO2
(8 × 1010# cm−3; measured), SO2 (1 × 109# cm−3;
es-timated) and CO (3.5 × 1012# cm−3; estimated). The temperature was further kept constant to a value of 303 K, while the start of the simulation was assumed to be at noon (local time). By dividing the initial concentration of isoprene by the summed maximum concentration of MVK and MACR, we estimate that the summed yield of MVK and MACR from oxidized isoprene is 0.35. This factor accounts for oxidation losses of MVK and MACR. A sensitivity test showed that the MVK/MACR yield response to a change in temperature and SO2concentration is minor.
For CO, a concentration change to 5 × 1013# cm−3 results in a 5 % lower yield. Due to the high NOx concentration
in Bosco Fontana, the reaction way via isoprene hydroxy hydroperoxides (ISOPOOH) does not form MVK/MACR.
3 Results and discussion
3.1 Comparison of procedures to identify detectable fluxes
In the following the data from 22 days of flux measurements are used by different analysis routines, and the results are compared. Negative fluxes are called downward fluxes, as it is not possible to differentiate between deposition and other sink terms such as chemical losses below the measurement height. The positive fluxes are called upward fluxes. The di-urnal cycles of the fluxes derived by the manual method and automated method (evaluated using a 10σnoisethreshold) are
shown in Fig. 4. The signal of the remaining masses with de-tectable flux (between 3 and 10σnoise)quantified by the
au-tomated method is summed up and plotted as “other”. The 24 h average fluxes of the different compounds and different methods are shown in Table 2.
The manual method identified the lower number of com-pounds (5), accounting for the lower total upward flux (8.53 nmol m−2s−1)and also the lower total downward flux (−0.28 nmol m−2s−1). The only compound with a flux that was deemed quantifiable by the manual method but not by the automated method with 10σnoise was C5H9O+2
(proto-nated), which contributed 1.2 % to the total downward flux and 0.1 % to the total upward flux.
The automated method with a 10σnoise threshold found
11 compounds with detectable flux and derived a to-tal upward flux of 9.6 nmol m−2s−1. The downward flux reached −0.48 nmol m−2s−1. If the 3σnoise threshold was
used, the number of compounds with detectable flux in-creased to 29 but the total upward flux was nearly the same (10.4 nmol m−2s−1), while the downward flux increased to −0.58 nmol m−2s−1. The main additional masses with de-tectable flux were acetone, acetaldehyde and acetic acid. As discussed in Sect. 2.3.2, fragments, water clusters and iso-topes identified during calibrations were removed from the data. The disregarded masses are shown in Table A1.
Figure 5 shows the diurnal variation of the net flux for the two approaches. The maximum difference in the hourly net flux between a 3σnoisethreshold and a 10σnoisethreshold was
less than 1.3 nmol m−2s−1and the daily average differed less than 0.5 nmol m−2s−1. The major difference lies in the num-ber of masses that are found to contribute to the total VOC flux: 29 (3σnoise), 22 (4σnoise), 22 (5σnoise), 19 (6σnoise), 15
(7σnoise), 14 (8σnoise), 12 (9 σnoise), and 11 (10σnoise).
The manual method is rather labor intensive, because the CCF must be checked for many different mass peaks (> 150, depending on the environment where the measurements are recorded) for several different times of the campaign (overall well over 1000 CCFs). Another weakness is that the defini-tion of a “clear maxima” is not objective and depends on the person who is working with the data. A positive aspect is that, during the manual evaluation of the data, possible problems or analysis faults can be detected more easily.
Figure 5. Diurnal net flux of the automated method (AM) with 3 and 10σnoise threshold for the flux calculation and the manual method. The uncertainty (root mean square) was calculated for the automated method with 10σnoise. The time is in CET wintertime.
Compared to the manual method, the automated method gives a fast and objective result and the quality of the fluxes can be selected by the different σnoisecriteria. However,
us-ing the absolute value of the signal changes the mean value of it and thereby reduces the variation and the standard devi-ation of the signal (see Figs. A4 and A5). For example, the standard deviation of the averaged absolute CCF was 21 % smaller than the standard deviation of the averaged CCF for acetone. This effect can be seen in the difference of the mean noise in Figs. A4 and A5. The higher mean noise corrects for the lower σnoise, but it is constant for different σnoisecriteria.
In the remainder of paper, all mentioned flux values were calculated using the automated method with 3σnoisethreshold
and the time zone used was CET wintertime (UTC +1 h). 3.2 Comparison to other studies
Comparing VOC concentration and especially fluxes from different locations (and/or times) is challenging as the results are dependent on ecosystem type, meteorology and the sur-roundings of the measurement site, as well as the instrumen-tal setup (e.g., inlet length).
The most obvious difference compared to the study by Park et al. (2013) was the number of masses showing fluxes. Park et al. (2013) found 494 (out of 664 masses) showed flux above the orange grove, whereas in this study only 29 (out of 163 masses) were found to have flux. While in both studies the results depended heavily on the σnoise threshold, in this
study the differences were much more subtle. In the study by Park et al. (2013), the number of ions with flux differed by 2 orders of magnitude. In our study, compounds which ful-filled the three 3σnoise criteria, but not the 10σnoise criteria,
contributed 16 % to the total downward flux, and 7 % to the total upward flux. However, the amount of compounds
fil-Table 2. Downward flux (df) and upward flux (uf) calculated with different methods. The individual compounds listed under automated are for a 10σnoisethreshold, the remaining compounds (between 3 and 10σnoise)are summed up under “other”.
Possible Mass Elemental Manual method Automated method
compound Th composition
% of total downward flux (df) or upward flux (uf) df (−0.28)1 uf (8.5)1 df (−0.58)1 uf (10.4)1 isoprenec 69.0699 C5H+9 0.2 75.0 0.0 62.4 methanolc 33.0335 C1H5O+1 97.3 18.4 63.2 14.8 acetonec 59.0491 C3H7O+1 1.7 3.3 MVK/MACRc 71.0491 C4H7O+1 1.1 3.9 0.8 3.0 acetaldehydec 45.0335 C2H5O+1 0.2 2.2 monoterpenesc 137.133 C10H+17 0.2 2.6 0.1 2.1 acetic acid2 61.0284 C2H5O+2 16.7 3.0 acroleinc 57.0335 C3H5O+1 0.3 0.8 hydroxyacetone 75.0441 C3H7O+2 0.8 0.7 C8GLV3 83.0855 C6H+11 0.0 0.3 pentanal 85.0648 C5H9O+1 0.1 0.2 unknown 101.0597 C5H9O+2 1.2 0.1 0 0 other (18) 16.2 7.0
1shows the total uf or df in nmol m−2s−1;2acetic acid was corrected for fragmentation (see Sect. 2.3.2);3C
6green leaf volatiles (GLV) are calculated via the fragment C6H+
11; the fragmentation pattern and the sensitivity of hexanal were used.
Figure 6. Average net flux of the major carbon emitters. Two masses were disregarded, as no matching elemental composition was found.
tered by the σnoise criteria changed from 29 (3σnoise)to 11
(10σnoise).
A comparison between the measured fluxes from this study and their values at the orange grove (Park et al., 2013) can be found in Table A2. The major difference was the large upward flux of isoprene measured in Bosco Fontana, which contributed 65 % to the total net flux of 9.8 nmol m−2s−1. This net flux of all compounds is twice as much as at the orange orchard. All the major compounds (> 8σnoise)from
the orange grove, except para-cymene, were also present in Bosco Fontana. Six of the remaining major compounds (> 8σnoise)of the orange grove had net fluxes which agree
within 50 % with the measured net fluxes in this study (iso-prene and MVK/MACR being the exception). The net carbon exchange in Bosco Fontana is shown in Fig. 6. Isoprene is the dominating compound and contributed 77 % to the carbon net flux, followed by the monoterpenes (5 %), MVK/MACR (3 %), methanol (3 %), acetone (2 %) and acetic acid (2 %). Overall, the measured and identified compounds had a net carbon flux of 41.8 nmol C m−2s−1. Two masses were not taken into account, as their elemental composition could not be determined (Table A2).
This net flux of carbon at Bosco Fontana is about 4 times higher than the values published by Park et al. (2013). If the net carbon emission of the measured compounds is com-pared to the net uptake of CO2during the measurement
pe-riod (2423 nmol C m−2s−1)the influence is less than 2 % in Bosco Fontana. The daily average value of CO2gas exchange
was −9.2 g C m−2, similar to what observed by Wilson and Baldocchi (2001) over a mixed deciduous forest.
3.3 Emission of terpenoids
The most abundant compound emitted by the Bosco Fontana forest was isoprene (protonated formula: C5H+9), comprising
over 60 % of the measured total upward flux. It had a clear diurnal cycle which follows the radiation. The maximum up-ward flux (diurnal) at 20.6 nmol m−2s−1occurred just after midday. Figure 7b shows the wind rose for the isoprene flux. There are more isoprene emitting plants to the west of the site, indicated by the highest fluxes coming from this direc-tion. Indeed, Acton et al. (2016) found that taking the
contri-Figure 7. (a) Wind rose of the wind speed. The percentages describe how often the wind came from the selected wind direction. (b) Unscaled wind roses for fluxes (left) and concentrations (right) of isoprene (top), monoterpenes (middle), and methanol (bottom).
bution of the strong emitters in this wind sector into account improved the correlation between predicted and measured isoprene fluxes. Similar behavior can be seen from the wind rose of the isoprene concentrations, although the extent of the forest is smaller towards southwest, providing less opportu-nity for isoprene to accumulate during advection. From 21:00 to 05:00 the upward flux stayed below 0.1 nmol m−2s−1. The main sink of isoprene is oxidation due to reactions with OH during daytime. The calculated isoprene lifetime for daytime conditions in Bosco Fontana was 2.2 h.
The fast oxidation, together with the relatively small ex-tent of the woodland in a mixed agricultural landscape with relatively low isoprene emissions, explains why the diurnal concentration maximum of isoprene was only 2.8 ppb, even though the isoprene upward flux was dominating all other measured VOCs. Its daily average concentration was 1.3 ppb. The emission factors of isoprene and monoterpenes in Bosco
Fontana, as well as the as the relative importance of pool and de novo emissions, are discussed in Acton et al. (2016). Isoprene’s major source globally is forests (Guenther et al., 1995); oaks are known for being isoprene emitters (Ras-mussen, 1970) and dominate European isoprene emissions. Potosnak et al. (2014) measured a maximum isoprene emis-sion of 217 nmol m−2s−1over an oak-dominated temperate forest in central Missouri.
2-Methyl-3-buten-2-ol (MBO) can dehydrate in the pro-ton transfer reaction and form isoprene (Fall et al., 2001; de Gouw and Warneke, 2006; Kaser et al., 2013). The influence on the isoprene signal depends on the MBO concentration and the settings of the PTR-ToF. The contribution of MBO to the isoprene signal in Bosco Fontana should be minor, as the major tree species are known to be isoprene or monoterpene emitters (König et al., 1995; Harley et al., 1999; Rosenstiel et al., 2002) as confirmed by the more specific leaf-level
mea-surements of Acton et al. (2016). However, a possible MBO source could be the understory of the forest.
In Bosco Fontana, monoterpenes have been the seventh most emitted “compound group” with a maximum diurnal upward flux of 0.7 nmol m−2s−1. Leaf-level measurements at Bosco Fontana presented by Acton et al. (2016) found the largest monoterpene emissions to be limonene originat-ing from Carpinus betulus and Corylus avellana and to some extent Cornus sanguinea, augmented with smaller emissions of α-pinene from Q. robur and Acer campestre, and β-pinene from A. campestre and C. betulus. Figure 7b shows the nor-malized wind rose of the monoterpenes upward flux (inde-pendent of the frequency of wind directions). The measure-ment site is very homogeneous, as no wind direction depen-dency on the monoterpenes flux was detected. This also holds for the monoterpene concentrations.
3.4 MVK/MACR and their sources
MVK and MACR have the same elemental composition (pro-tonated formula: C4H7O+)and cannot be separated with our
instrument settings. In Bosco Fontana, 3 % of the total up-ward VOC flux was due to MVK/MACR, which are both oxidation products of isoprene. To give an estimate of how much of the MVK/MACR flux is likely to have originated from atmospheric oxidation of isoprene below the measure-ment level zm, we used two methods to estimate the flux
di-vergence. The oxidation of isoprene is dominated by the re-action with the OH radical. The daytime maximum flux of isoprene oxidation products between ground level and mea-surement height were around 0.61 nmol m−2s−1 (Eq. 14) and 0.24 nmol m−2s−1(Eq. 15) if the lower limit is the no-tional height. However, the result of the integration (Eq. 13) varies considerably depending on the integration domain and the assumed profiles.
Another approach to estimate the chemical degradation is to use the look-up tables for flux-to-surface-exchange (F /E) ratios created using a stochastic Lagrangian transport model by Rinne et al. (2012). For the F /E ratio we use typical day-time values of friction velocity and chemical lifeday-time of iso-prene. Depending on the assumed oxidant profile and leaf area index, F /E ratios ranged between 0.97 and 0.95. Mul-tiplying the isoprene upward flux by the F /E ratio leads to the oxidation fluxes between 0.6 and 1.0 nmol m−2s−1.
According to our calculations (Sect. 2.4), 35 % of the oxidized isoprene molecules will create MVK or MACR molecules (for midday conditions). The scatter plot between the measured MVK/MACR flux and the calculated source of MVK/MACR by the oxidation of isoprene below the mea-surement height (Fig. 8) shows a correlation coefficient of 0.81. This correlation, however, does not necessarily imply causality. The biogenic VOC emissions and concentrations, as well as the concentration of OH radicals, are light depen-dent, which may lead to correlations where causality does not exist.
Figure 8. Scatter plot of the calculated MVK/MACR flux from oxi-dation of isoprene and the observed MVK/MACR flux. The correla-tion factor for the data is 0.81. The 1 h data are separated to day and night values by a 200 µmol m−2photosynthetically active radiation (PAR) threshold. The yx ratios of the daytime data have been cal-culated to determine the median and the 25th and 75th percentiles. The MVK/MACR flux from oxidation of isoprene was calculated using Eq. (14).
The hourly data in the plot were separated into day and night by using a 200 µmol m−2s−1 photosynthetically ac-tive radiation threshold. Then the yx ratios of the daytime data were used to calculate the median and percentile ra-tios. The influence of the oxidation of isoprene to the mea-sured MVK/MACR flux was estimated from the 25th and 75th percentiles ratios. If Eq. (14) was used to calculate this influence, the oxidation products of isoprene vary between 10 and 30 % of the MVK/MACR flux. If Eq. (15) was used, the contribution of isoprene to the MVK/MACR flux varied between 4 and 10 %.
Comparing the results of the F /E calculations with the maximum diurnal MVK/MACR flux of 1.3 nmol m−2s−1 suggested that a contribution of 16 to 27 % of the MVK/MACR flux may originate from atmospheric chem-istry. Overall the oxidation of isoprene may have an impor-tant influence (4 to 30 %) on the MVK/MACR flux, but it fails to explain it fully.
Fluxes can also originate from direct MVK/MACR emis-sions from the plant as shown by Jardine et al. (2012). Other studies have shown also minor (Fares et al., 2015) or negligi-ble (Karl et al., 2009, 2010) emission of MVK/MACR; how-ever, in these studies there was a net uptake to the leaf. Part of the MVK/MACR concentration and fluxes may also be mis-attributed fragments from higher oxygenated hydrocarbons (Liu et al., 2013; Rivera-Rios et al., 2014). MVK and MACR have also been found to be formed from the decomposition of hydroxyl hydroperoxides (ISOPOOH) in the PTR-MS in-let. However, the ISOPOOH precursor ISOPO2 will be
effec-tively quenched by NO. Therefore the ISOPOOH concentra-tion in polluted environments such as the Po Valley would be expected to be very low, and consequently this artifact can be ruled out at this location. Additionally, an existing inter-ference by ISOPOOH would most likely lead to unrealistic MVK/MACR deposition, due to an expected downward flux of peroxides (Nguyen et al., 2015).
In general, a comprehensive theory of MVK/MACR emis-sion and deposition is lacking, while in some environments (especially in the tropics) MVK/MACR is found to deposit fast, approaching the maximum rate permitted by turbulence (i.e., with a small canopy uptake resistance; cf. Misztal et al., 2011, and references therein), while in other environments, like at Bosco Fontana, upward fluxes are observed.
3.5 Emission of oxygenated VOCs
The second-most emitted compound was methanol (proto-nated formula: CH5O+), whose upward flux started at 08:00,
later compared with the remaining VOCs. It contributed 15 % to the total upward flux (maximum at 14:30 with 4.4 nmol m−2s−1). Methanol is mostly emitted by plants, e.g., by the plant growth metabolism (Wohlfahrt et al., 2015). From the wind rose in Fig. 7b it can be seen that the highest upward fluxes of methanol originated from the west.
The third-most emitted compound was acetone, which had a diurnal maximum upward flux at 11:30 with 1.0 nmol m−2s−1. Its daily average contributed over 3 % to the total upward flux (daily average). It has the same ele-mental composition as propanal. However, the contribution of propanal to the signal is normally less than 10 % (de Gouw and Warneke, 2006). Acetone sources are ubiquitous: it can be emitted from several plants and trees (Geron et al., 2002; Fall, 1999), as well as from anthropogenic processes (Singh et al., 1994), or produced through secondary photochemical production (Goldstein and Schade, 2000).
The upward flux of acetaldehyde peaked around 11:30 at 0.7 nmol m−2s−1. It is a hazardous air pollutant (EPA, 1994), and plays an important role in the formation of ozone, HOx
radicals (Singh et al., 1995) and PAN (Roberts, 1990). The maximum upward flux of acetic acid was at 11:30 at 1.0 nmol m−2s−1. Its sources are manifold: it is emitted by soil, vegetation and livestock; it can be produced photochem-ically; and it is also a combustion marker for biomass and fossil fuels (Chebbi and Carlier, 1996).
The C6green leaf volatiles (GLVs; C6H13O+)are
emit-ted by damaged plants seconds after the damage occurred (Holopainen, 2004). They are found to be important in “plant communication” and are used as “plant indirect defenses” (Scala et al., 2013, and references therein). The emission of GLVs accounted 1.2 % of the total emission. Similar to the monoterpenes, a fragment of GLV (C6H+11; Table 2) was
also measured. But, unlike monoterpenes, the behavior of the parental ion and fragment is very different. While C6H13O+
has a downward flux / upward flux ratio of 0.3 (Sect. 3.6), the
Figure 9. Diurnal average of the total VOC concentration resolved with the PTR-ToF (filtered for fragments). The 10 most abundant flux compounds are shown, the remaining compounds are summed up and plotted as “other”. The time is in CET wintertime.
fragment’s downward flux / upward flux ratio is < 1 %. This leads to the assumption that the fragmentation pattern for the main GLVs measured in Bosco Fontana is different.
Ethanol contributed 1.2 % to the total upward flux. Ethanol is known to be emitted from different ecosystems, as shown in Park et al. (2013) and Kaser et al. (2013).
Other reported sources of carbonyls include conifers (e.g., Janson et al., 1999; Rinne et al., 2007) and decaying vegeta-tion (e.g., de Gouw et al., 2000; Karl et al., 2001; Warneke et al., 2002). The remaining compounds each contributed less than 1 % to the total upward flux.
3.6 VOC deposition
In the case of wet or dry deposition, the ambient concen-tration of the deposited compound plays an important role. Figure 9 shows the total VOC concentration detected by the PTR-ToF and its diurnal behavior. The highest total VOC concentrations occur during the night, when the planetary boundary layer is shallower and the volume, into which the VOCs are emitted, is smaller. However, concentrations of biogenic VOCs (e.g., isoprene, monoterpenes) were much smaller during the night, reflecting a combination of smaller emissions and influences from air from outside the forest (the footprint for concentration measurements is much larger than the Bosco Fontana forest). By contrast, long-lived com-pounds, which can also be of anthropogenic origin, increase in concentration at night. One of them, methanol, showed the largest concentration and biggest downward flux (Table 3). The wind rose for the methanol concentration is shown in Fig. 7b. The largest concentrations were measured when the wind direction was northeast and southwest. The downward flux of methanol generally lasted from 01:00 to 08:00. The
Table 3. Compounds with downward flux, their daily averaged concentration, daily averaged downward flux, deposition velocity and respec-tive lifetimes.
Compound Concentration Downward flux Lifetime Deposition velocity [ppb] [nmol m−2s−1] [days] [mm s−1] methanol 14.3 0.36 9a 0.56 acetic acid 4.9 0.10 1.7b 0.50 C6GLV (C6H13O+1) 0.8 0.04 – 0.15 ethanol 0.6 0.02 2.8c 0.55 acetone 4.7 0.01 15d 0.05 73.0255 0.2 0.01 – 1.4
aAtmospheric lifetime according to Heikes et al. (2002).bLifetime in the boundary layer according to Paulot et al. (2011). cAtmospheric lifetime according to Naik et al. (2010).dTropospheric lifetime according to Jacob et al. (2002).
methanol concentration also peaked during this period, in-dicating that the concentrations were considerably affected by horizontal transport or secondary production. Since the largest concentrations were measured at the same time when downward fluxes were observed, the source must have been located outside of the flux footprint. However, as the wind speed was below 2 m s−1, a major source is probably close to the measurement site. Overall methanol accounted for 63 % to the total downward flux. The main sinks of methanol are reactions with OH and dry and wet deposition, which restrict the atmospheric lifetime of methanol to nine days (Heikes et al., 2002). Heikes et al. (2002) determined the average lifetime due to the gas reactions with OH to 18 days, while the lifetime with respect to deposition was calculated to be 24 days. Applying the same procedure for Bosco Fontana results in a deposition lifetime of 280 days, which reflects the different deposition velocities (Vd) used. For their
es-timate Heikes et al. (2002) used Vd=4 mm s−1, while in
Bosco Fontana, the average deposition velocity was an order of magnitude lower, 0.55 mm s−1. As the measured down-ward flux is the sum of deposition and emission, just an upper limit of the deposition lifetime can be given. Additionally, the ambient humidity during the campaign was on average 55 %, which limits wet surfaces and thereby dry deposition. The global average also includes deposition over oceans, which is twice as fast as over land (Heikes et al., 2002).
The deposition of methanol has been observed in other studies (e.g., Holzinger et al., 2001; Goldan et al., 1995, Riemer et al., 1998; Rantala et al., 2015; Wohlfahrt et al., 2015). Laffineur et al. (2012) observed considerable methanol uptake, which they suggest to be caused by ad-sorption/desorption to water films. In the bottom panel of Fig. 10, the ambient, aerodynamic and dew point tempera-ture are shown. The colored areas in the figure mark the stan-dard deviation of the calculated temperatures. On the onset of methanol downward flux periods (Fig. 10 upper panel), the dew temperature and aerodynamic temperature are closest to each other and the relative humidity is around 65 %. The for-mation of dew is expected to happen during this time (01:00 to 06:00). Interestingly, the downward flux increases during
Figure 10. Diurnal exchange of the methanol (top panel) and the calculated aerodynamical (Taero), dew point (Tdew) and ambient temperature (Tambient; bottom panel). The blue and red shaded ar-eas indicate the standard deviation of the calculated temperatures and the dashed line is the relative humidity.
the night and reaches a maximum between 07:00 and 08:00, when the relative humidity has already decreased to < 60 % and the difference between aerodynamic and dew point tem-perature is around 10◦C. After 08:00 the emissions dominate over the deposition, and for the rest of the day the methanol flux is positive.
Acetic acid showed the second highest downward flux, which contributed more than 16 % to the total downward flux. It also had the second highest concentration (Table 3). Its lifetime is about 1.7 days in the boundary layer (Paulot et al., 2011).
With the elemental composition of C6H13O+(protonated)
this C6GLV shows a higher downward flux (compare to its
fragment C6H+11), which explains 6 % of the negative flux.
Ethanol had its maximum downward flux at 07:00 with −0.11 nmol m−2s−1, and it explains 3.5 % of the total down-ward flux.
Next is acetone, which accounted for 1.7 % to the total downward flux. It had the third highest concentration (Ta-ble 3) and the tropospheric lifetime is reported to be 15 days (Jacob et al., 2002).
An unidentified compound with the mass 73.0255 amu caused 1.6 % of the total downward flux, while the remaining compounds each contributed less than 1 %.
4 Conclusions
During the Bosco Fontana campaign, up to 29 (depending on method and sigma threshold) compounds were found to have a detectable flux. The VOC exchange was dominated by isoprene which comprised over 65 % of the total net flux (on a molar basis). The high isoprene flux influenced the MVK/MACR flux via atmospheric oxidation. The cal-culated chemical production was able to explain up to 30 % of the measured MVK/MACR flux. Thus, the major part of the MVK/MACR flux remained unaccounted and further re-search is needed to identify its sources.
Methanol caused over 60 % of the total downward flux, which happened during the early morning. The removal was assumed to be dry deposition to water films on surfaces (in-cluding dew) as the downward fluxes coincided with the cal-culated ratios of dew point to aerodynamic temperature ap-proaching unity. The deposition lifetime of methanol was es-timated to be long compared to the global mean, which might be explained by the dry conditions at Bosco Fontana during the measurement period.
Overall, five compounds caused over 90 % of the to-tal downward flux (−0.58 nmol m−2s−1), and seven com-pounds add up to over 90 % of the total upward flux (10.4 nmol m−2s−1). The measured VOCs contribute with less than 2 % to the net exchange of carbon by CO2.
Comparing the results with the emissions from an orange grove (Park et al., 2013), our study found far fewer com-pounds that showed significant exchange. The sigma criteria (3–10) used for the compound with detectable flux classifica-tion had only a minor effect on total VOC upward and down-ward fluxes. The largest difference in the net VOC exchange between Bosco Fontana and the orange orchard was the dom-inance of isoprene upward flux at Bosco Fontana, while the fluxes of other major compounds were comparable between the two measurement sites.
The manual method, which searches for CCF maxima manually, detected over 80 % of the upward flux, 49 % of the downward flux and 84 % of the net flux compared with the automated method, which uses a routine to find masses with flux. Thus, this study recommends the automated method, as the fast analysis, objective criteria and better flux detection are valuable assets for calculating and classifying fluxes of several hundred different ion peaks.
Appendix A
Table A1. Information about all 164 measured mass peaks in Bosco Fontana. The limit of detection is given for the calibrated masses. Masses with an F in the last column are filtered out as they were identified as fragments or clusters.
Mass Elem. Background Sigma LOD (30 min);
[amu] comp. threshold frag.
21.0221 H318O+1] 1 8.29 27.0229 C2H+3 1 −0.09 28.0056 N+2 1 1.94 29.0134 H1N+2 1 35.03 29.9974 O1N+1 1 3.04 31.0178 C1H3O+1 1 6.12 31.9893 O+2 1 162.67 32.9971 H1O+2 1 3.42 33.0335 C1H5O+1 0 45.69 38.5 35.0366 H5O1N+1 0 8.77 36.0206 H4O+2 1 1.98 36.0444 H6O1N+1 1 19.26 37.0284 H5O+2 1 36.46 38.0362 H6O+2 1 159.67 39.9629 1 1.42 40.9710 1 1.74 41.0386 C3H+5 0 156.77 F 42.0100 C2H2O+1 0 −1.53 42.0338 C2H4N+1 0 6.26 5.1 43.0178 C2H3O+1 0 10.57 F 43.0542 C3H+7 0 5.13 44.0138 0 1.74 44.9971 C1H1O+2 1 2.63 45.0335 C2H5O+1 0 15.53 13.2 45.9924 O2N+1 1 3.27 46.0287 C1H4O1N+1 0 2.46 47.0128 C1H3O+2 0 2.92 47.0240 H3O1N+2 1 59.34 47.0491 C2H7O+1 0 8.74 48.0080 H2O2N+1 1 4.87 49.0284 C1H5O+2 0 1.31 49.9998 H2O+3 1 −0.34 51.0077 H3O+3 1 5.73 51.0441 C1H7O+2 0 26.53 F 51.9382 1 −1.63 51.9944 C3O+1 1 −0.68 53.0022 C3H1O+1 1 −0.30 53.0386 C4H+5 0 3.59 53.9394 1 −0.14 55.0390 H7O+3 1 167.77 55.9377 1 2.88 56.0468 H8O+3 1 7.35 57.0335 C3H5O+1 0 19.26 6.9 57.0699 C4H+9 1 12.76 57.9352 1 −0.86 59.0491 C3H7O+1 0 15.82 6.1 60.0481 0 1.42 61.0284 C2H5O+2 0 11.79 62.0237 C1H4O2N+1 0 0.84 63.0263 C2H7S+1 0 1.60 63.9852 H2O1N1S+1 0 −1.30 65.0233 C1H5O+3 0 5.84
Table A1. Continued. 65.0584 H7O1N + 3 0 1.59 67.0542 C5H+7 0 64.76 F 68.0621 C5H+8 0 96.03 F 69.0699 C5H+9 0 241.62 2.8 71.0491 C4H7O+1 0 39.95 4.7 71.0851 C5H+11 0 7.47 72.0875 0 6.68 73.0255 0 3.63 73.0473 0 0.73 73.0648 C4H9O+1 0 9.64 2.1 74.0227 0 0.75 75.0441 C3H7O+2 0 11.07 75.9436 C1S+2 1 0.51 77.9424 1 −0.60 79.0542 C6H+7 0 3.44 2.5 80.9971 C4H1O+2 0 −0.80 81.0335 C5H5O+1 1 10.76 81.0699 C6H+9 0 106.38 F 83.0523 0 1.47 83.0855 C6H+11 0 15.80 85.0284 C4H5O+2 0 1.50 85.0648 C5H9O+1 0 12.14 85.1012 C6H+13 0 1.30 85.9471 1 1.27 87.0441 C4H7O+2 0 1.58 87.0804 C5H11O+1 0 1.61 88.0763 0 1.14 88.9555 H3O1Cl+2 1 0.42 89.0233 C3H5O+3 0 3.58 89.0597 C4H9O+2 0 0.99 90.9487 1 7.58 91.0567 0 1.16 91.9457 1 1.79 92.9480 1 2.59 93.0369 C3H9O1S+1 1 6.19 93.0699 C7H+9 0 1.79 0.7 93.9542 C1H2O1S+2 1 −2.91 95.0161 C2H7O2S + 1 0 −0.06 95.0478 C4H5N+3 0 1.20 95.0855 C7H+11 0 8.87 F 95.9512 O4S+1 1 1.38 96.9961 0 1.10 97.0284 C5H5O+2 0 3.04 97.0634 C4H7N+3 0 2.96 97.1012 C7H+13 0 1.00 98.0237 C4H4O2N+1 1 −1.51 98.0600 C5H8O1N+1 0 1.43 99.0077 C4H3O+3 0 0.34 99.0441 C5H7O+2 0 6.23 99.0804 C6H11O+1 0 6.69 100.0393 C4H6O2N+1 1 0.08 100.0757 C5H10O1N+1 1 −0.90 101.0233 C4H5O+3 0 −1.69 101.0597 C5H9O+2 0 5.07 101.0961 C6H13O+1 0 3.11 18.7 101.9428 0 0.35 102.0913 C5H12O1N+1 0 0.57 102.9468 0 −0.23 103.0390 C4H7O+3 0 1.35
Table A1. Continued. 103.0754 C5H11O+2 0 2.90 103.9516 H5S2Cl+1 1 0.17 105.9359 1 1.19 106.9418 1 0.07 106.9617 0 0.61 107.0491 C7H7O+1 0 1.47 107.0855 C8H+11 0 0.58 0.4 107.9512 C1O4S+1 1 2.75 108.9590 C1H1O4S+1 1 2.88 108.9920 C5H1O+3 1 −0.03 109.0284 C6H5O+2 0 −3.77 109.0648 C7H9O+1 0 2.71 109.1012 C8H+13 0 1.06 111.0441 C6H7O+2 1 1.08 111.0804 C7H11O + 1 1 1.28 111.1168 C8H+15 0 −0.31 111.9461 O5S+1 1 −0.89 113.0597 C6H9O+2 0 −0.83 113.0947 C5H11N+3 0 2.37 115.0096 1 1.31 115.0363 C1H3O1N+6 0 −1.02 115.0754 C6H11O+2 0 2.18 115.1117 C7H15O+1 0 −0.24 116.9060 C1Cl+3 0 −0.28 117.9542 C3H2O1S+2 1 −2.30 118.9451 0 −3.19 119.9512 C2O4S+1 1 −0.03 120.9534 0 −1.06 121.0648 C8H9O+1 0 0.10 121.1012 C9H+13 0 0.78 0.4 123.0441 C7H7O+2 1 0.20 123.1168 C9H+15 0 1.78 123.9440 1 10.14 124.9510 C1O4N1Cl+1 1 −1.66 125.9572 1 −0.85 126.0159 C2H6O+6 1 1.45 126.0557 1 1.71 126.0957 1 0.30 127.0390 C6H7O+3 0 1.84 127.0754 C7H11O+2 1 −1.93 127.1117 C8H15O+1 0 −0.12 128.1107 1 −0.39 130.9920 1 1.39 135.0406 0 4.35 137.0557 0 2.16 137.1325 C10H+17 0 115.45 0.5 138.0590 1 30.15 140.0304 1 1.14 140.0751 1 −1.76 144.9606 C6H3Cl+2 1 0.69 145.9685 C6H4Cl+2 1 −0.02 180.9373 Cl3C6H+4 1 2.40
Figure A1. Thirty-minute cross covariance function of C5H+9 from 15 June 2012, 14:15. The function was normalized to the maximum. A clear maximum can be seen, slightly shifted to the right by the lag time.
Figure A2. Thirty-minute normalized cross covariance function of C1H4O+1 from 22 June 2012, 04:45. A clear minima can be seen, which defines downward fluxes.
Figure A3. Smoothed normalized CCF of C3H7O+1. The red cross marks the lag time used in the original CCF (Fig. A4) to find the flux value; the allowed lag time window is from 0 to 5 s. In this specific CCF only a local maximum was found, as there is no clear maximum.
Table A2. Comparison of the detected compounds with flux in Bosco Fontana and their values at the orange grove (Park et al., 2013). Elemental Mass Net (downward/upward) flux
composition (24 h average) [nmol m−2s−1]
[amu] this study Park et al. (2013)1 C1H5O+1 33.0335 1.168 (−0.365/1.533) 1.655 (−0.102/1.757) H5O1N+1 35.0366 0.061 (−0.002/0.064) C2H4N+1 42.0338 0.046 (−0.005/0.051) C3H+7 43.0542 0.079 (−0.005/0.084) 0.075 (−0.001/0.076) C2H5O+1 45.0335 0.228 (−0.001/0.229) 0.133(−0.016/0.148) C2H7O+1 47.0491 0.105 (−0.02/0.125) −0.013(−0.017/0.004) C4H+5 53.0386 0.008 (−0.001/0.009) 0.012(−0.009/0.021) C3H5O+1 57.0335 0.085 (−0.002/0.086) 0.033 (−0.016/0.049) C3H7O+1 59.0491 0.335 (−0.01/0.345) 0.281(−0.004/0.286) C2H5O+2 61.0284 0.214 (−0.096/0.311) 0.413 (−0.005/0.418) C1H5O+3 65.0233 0.03 (−0.001/0.031) C5H+9 69.0699 6.466 (0/6.466) 0.025 (−0.001/0.025) C4H7O+1 71.0491 0.311 (−0.004/0.315) 0.041 (−0.004/0.044) C5H11+ 71.0851 0.02 (−0.001/0.021) 0.006 (−0.005/0.011) unknown 72.0875 0.015 (−0.002/0.016) unknown 73.0255 0.026 (−0.009/0.035) C4H9O+1 73.0648 0.075 (−0.002/0.077) 0.029 (−0.007/0.036) C3H7O+2 75.0441 0.068 (−0.005/0.073) C6H+7 79.0542 0.021 (−0.002/0.023) 0.016 (−0.002/0.018) C6H+11 83.0855 0.034 (0/0.035) 0.007 (−0.004/0.011) C5H9O+1 85.0648 0.017 (−0.001/0.018) 0.008 (−0.005/0.013) C3H5O+3 89.0233 0.011 (−0.001/0.012) 0.007 (−0.004/0.011) C5H5O+2 97.0284 0 (−0.002/0.002) 0.007 (−0.005/0.012) C4H7N+3 97.0634 0.003 (−0.001/0.004) 0.007 (−0.007/0.015) C5H7O+2 99.0441 0.017 (−0.001/0.018) 0.008 (−0.004/0.012) C6H11O+1 99.0804 0.014 (−0.001/0.014) C5H9O+2 101.0597 0.014 (−0.001/0.014) 0.004 (−0.003/0.008) C6H13O+1 101.0961 0.091 (−0.037/0.128) C10H+17 137.1325 0.219 (−0.001/0.219) 0.235 (0/0.236)2 1The presented data were calculated from Table S2 in the Supplement.2The fragments of monoterpenes were summed up to be comparable with the upscaled monoterpene signal from Bosco Fontana.
Figure A4. Thirty-minute normalized cross covariance function of C3H7O+1 from 15 June 2012, 14:15. No clear maximum or mini-mum can be seen around 0 s lag time. The red lines at the corners of the plot indicate the average noise, while the cyan lines are the 3σnoisethreshold (in the legend the 62 % represents just the 3σnoise, while in the plot it is added to the mean noise).
Cr
La
Mean
Figure A5. Thirty-minute absolute normalized cross covariance function of C3H7O+1 from 15 June 2012, 14:15. The red lines at the corners of the plot indicate the average noise, while the cyan lines are the 3σnoisethreshold (in the legend the 39 % represents just the 3σnoise, while in the plot it is added to the mean noise). Taking the absolute of a CCF increases the mean value and thereby reduces the standard deviation of the noise (compare with Fig. A4).
Figure A6. Averaged absolute CCF for C5H+9. The flux used is well over the 10σnoisecriteria.
Figure A7. Averaged absolute CCF for C3H7O+2. Due to the con-stant lag time, the maximum of the CCF is not always used, as this would overestimate the flux. In this case the value used is actually a local minimum between two maxima.
Figure A8. Averaged absolute CCF of C4H+5, which fulfilled the 3σnoisecriteria.
Figure A9. Averaged absolute CCF of mass 71.0851 (could not be identified), which fulfilled the 7σnoisecriteria.
